The number of published scientific paper grows rapidly each year, totaling more than 2.9 million annually. New methodologies and systems have been developed to analyze scientific production and performance indicators from large quantities of data available from the scientific databases, such as Web of Science or Scopus. In this paper, we analyzed the international scientific production and co-authorship patterns for the most productive authors from Serbia based on the obtained Web of Science dataset in the period 2006–2013. We performed bibliometric and scientometric analyses together with statistical and collaboration network analysis, to reveal the causes of extraordinary publishing performance of some authors. For such authors, we found significant inequality in distribution of papers over journals and countries of co-authors, using Gini coefficient and Lorenz curves. Most of the papers belong to multidisciplinary, interdisciplinary, and the field of applied sciences. We have discovered three specific collaboration patterns that lead to high productivity in international collaboration. First pattern corresponds to mega-authorship papers with hundreds of co-authors gathered in specific research groups. The other two collaboration patterns were found in mathematics and multidisciplinary science, mainly application of graph theory and computational methods in physical chemistry. The former pattern results in a star-shaped collaboration network with mostly individual collaborators. The latter pattern includes multiple actors with high betweenness centrality measure and identified brokerage roles. The results are compared with the later period 2014–2023, where high scientific production has been observed in some other fields, such as biology and food science and technology.

Heterogeneous architectures consisting of both central processing units and graphics processing units are common in contemporary computer systems. For that reason, several programming models have been developed to exploit available parallelism, such as low-level CUDA and OpenCL, and directive-based OpenMP and OpenACC. In this paper we explore and evaluate the applicability of OpenACC, which is a directive-based programming model for GPUs. We focus both on the performance and programming effort needed to parallelize the existing sequential algorithms for GPU execution. The evaluation is based on the benchmark suite Parboil, which consists of 11 different mini-applications from different scientific domains, both compute- and memory-bound. The results show that mini-apps parallelized with OpenACC can achieve significant speedups over sequential implementations and in some cases, even outperform CUDA implementations. Furthermore, there is less of a programming effort compared to low-level models, such as CUDA and OpenCL, because a majority of the work is left to the compiler and overall, the code needs less restructuring.

Large language models have sparked a lot of attention in the research community in recent days, especially with the introduction of practical tools such as ChatGPT and Github Copilot. Their ability to solve complex programming tasks was also shown in several studies and commercial solutions increasing the interest in using them for software development in different fields. High performance computing is one of such fields, where parallel programming techniques have been extensively used to utilize raw computing power available in contemporary multicore and manycore processors. In this paper, we perform an evaluation of the ChatGPT and Github Copilot tools for OpenMP-based code parallelization using a proposed methodology. We used nine different benchmark applications which represent typical parallel programming workloads and compared their OpenMP-based parallel solutions produced manually and using ChatGPT and Github Copilot in terms of obtained speedup, applied optimizations, and quality of the solution. ChatGPT 3.5 and Github Copilot installed with Visual Studio Code 1.88 were used. We concluded that both tools can produce correct parallel code in most cases. However, performance-wise, ChatGPT can match manually produced and optimized parallel code only in simpler cases, as it lacks a deeper understanding of the code and the context. The results are much better with Github Copilot, where much less effort is needed to obtain correct and performant parallel solution.

Graphics processing units and similar accelerators have been intensively used in general purpose computations for several years. In the last decade, GPU architecture and organization changed dramatically to support an ever-increasing demand for computing power. Along with changes in hardware, novel programming models have been proposed, such as NVIDIA's Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) by Khronos group. Although numerous commercial and scientific applications have been developed using these two models, they still impose a significant challenge for less experienced users. There are users from various scientific and engineering communities who would like to speed up their applications without the need to deeply understand a low-level programming model and underlying hardware. In 2011, OpenACC programming model was launched. Much like OpenMP for multicore processors, OpenACC is a high-level, directive-based programming model for manycore processors like GPUs. This paper presents an analysis of OpenACC programming model and its applicability in typical domains like image processing. Three, simple image processing algorithms have been implemented for execution on the GPU with OpenACC. The results were compared with their sequential counterparts, and results are briefly discussed.

Rapid development of modern central processing units (CPUs) and graphics processing units (GPUs) has allowed a significant increase in computing power for different engineering applications. Audio signal processing is an example of such a computationally demanding application. Fast Fourier Transform (FFT) is often a core part of these processing algorithms, and it is efficiently implemented on the CPUs and GPUs through available libraries. In this paper, we present an implementation of the convolution reverb effect using OpenMP and FFTW library on the CPU, and CUDA and cuFFT library on the GPU. Implemented effect is tested with the set of four different audio signals and ten impulse responses of different lengths. We observed speedups in the range of 2 to 3 times over CPU implementation. The results of the analysis are briefly discussed with emphasis on the benefits and drawbacks of using GPUs in such an application.

Plants are crucial in providing sufficient food for the increasing global population. To be able to provide an appropriate amount of food, the maximization of agricultural output is needed while input needs to be minimized. For these purposes, plant phenotyping techniques, i.e., measuring and analyzing the physical and biochemical characteristics of plants, can be employed. One of the most essential indicators of the general health and development of the plant is the color, shape, and number of leaves. To analyze plant images and capture essential plant traits, various algorithms have been developed. However, one of the important challenges in developing these algorithms is the occlusion or overlapping of leaves and biomass. We present a solution for leaf counting in the presence of occlusion in the plant Arabidopsis thaliana that includes four different convolutional neural network architectures. Datasets from the Computer Vision Problems in Plant Phenotyping (CVPPP) 2017 challenge and Photon System Instruments were used. The results are discussed in detail and compared with the existing solutions. Results showed that our solutions for leaf counting are superior to the previous winners of the CVPPP challenges.